Anatomy segmentation through low-resolution multi-atlas label fusion and corrective learning

ABSTRACT

Computationally efficient anatomy segmentation through low-resolution multi-atlas label fusion and corrective learning is provided. In some embodiments, an input image is read. The input image has a first resolution. The input image is downsampled to a second resolution lower than the first resolution. The downsampled image is segmented into a plurality of labeled anatomical segments. Error correction is applied to the segmented image to generate an output image. The output image has the first resolution.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a divisional of U.S. application Ser. No.15/253,326, filed on Aug. 31, 2016, which is hereby incorporated byreference in its entirety.

BACKGROUND

Embodiments of the present invention relate to anatomy segmentation, andmore specifically, to computationally efficient anatomy segmentationthrough low-resolution multi-atlas label fusion and corrective learning.

BRIEF SUMMARY

According to embodiments of the present disclosure, methods of, andcomputer program products for, anatomy segmentation are provided. Aninput image is read. The input image has a first resolution. The inputimage is downsampled to a second resolution lower than the firstresolution. The downsampled image is segmented into a plurality oflabeled anatomical segments. Error correction is applied to thesegmented image to generate an output image. The output image has thefirst resolution.

According to other embodiments of the present disclosure, methods of,and computer program products for, anatomy segmentation are provided. Atarget image and each one of a series of training images are resampledfrom an original resolution to a lower resolution. After resampling, thetarget image is compared against each one of the series of trainingimages, through registration and warping, thereby producing a labeledtarget image for each of the training images. The labeled target imagesare weighted-averaged to form a consensus labeled target image. Theconsensus labeled target image is resampled from an original resolutionto a higher resolution. Error correction is applied to the consensuslabeled target image, thereby forming a finalized labeled target image.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

FIG. 1A is an exemplary unlabeled CT image.

FIG. 1B depicts an exemplary manual labelling of the image of FIG. 1A.

FIGS. 2A-C depict segmentations generated by multi-atlas label fusion at1 mm, 3 mm, and 5 mm, respectively.

FIGS. 3A-C depict segmentations generated by multi-atlas label fusionwith corrective learning at 1 mm, 3 mm, and 5 mm, respectively.

FIG. 4 is an exemplary manual labelling of a CT image.

FIGS. 5A-C depict segmentations generated by multi-atlas label fusion at1 mm, 3 mm, and 5 mm, respectively.

FIGS. 6A-C depict segmentations generated by multi-atlas label fusionwith corrective learning at 1 mm, 3 mm, and 5 mm, respectively.

FIG. 7 illustrates an exemplary segmentation pipeline according toembodiments of the present disclosure.

FIG. 8 illustrates an exemplary method for segmentation according toembodiments of the present disclosure.

FIG. 9 depicts a computing node according to embodiments of the presentinvention.

DETAILED DESCRIPTION

Deformable registration based multi-atlas segmentation is suitable for abroad range of anatomy segmentation applications. However, thisperformance comes with a high computational burden due to therequirement for deformable image registration and voxel-wise labelfusion. To address the high computational cost problem, the presentdisclosure provides for application of multi-atlas segmentation in lowresolution space followed by refinement of the results by learning-basederror correction in the native image space. In a cardiac CT segmentationapplication, methods according to the present disclosure not onlysignificantly reduce the computational cost for deformableregistration-based multi-atlas segmentation, but also produce moreaccurate segmentation.

Multi-atlas segmentation applies image registration to propagateanatomical labels from pre-labeled atlases to a target image and applieslabel fusion to resolve conflicting anatomy labels produced by warpingmultiple atlases. For anatomical structures that can be aligned acrosssubjects through image registration, multi-atlas segmentation thatemploys deformable image registration for label propagation is one ofthe most competitive anatomy segmentation techniques.

Despite the segmentation performance, the high computational cost is alimiting factor on application of the technique to large scale problems.This problem is partially addressed by the increasing cost-effectivecomputational powers brought by high performance computing technologies.When pairwise image registrations between each atlas and a target imagecan be computed fully in parallel, the overall turnaround for applyingmulti-atlas segmentation to segment one image equals the time for asingle deformable registration plus the time for label fusion. However,both deformable image registration and label fusion still can take hourson processing a 3D volume data.

In order to make multi-atlas segmentation more suitable for large scalestudies, addressing its high computational cost is necessary. Forexample, atlas selection may be applied for improving the speed andaccuracy of label fusion. To address the high computational cost raisedfrom employing deformable image registration, nonlocal label fusiontechniques are developed to only work with affine/linear registrationsfor brain tissue segmentation problems. However, affine/linearregistration is often inadequate for applications where large non-lineardeformations exist, such as applications in cardiac and abdominalregions.

A reduction in the total number of registrations required for eachsegmentation task may also reduce the overall computation time. Forexample, machine learning may be applied to predict the accuracy ofdeformable registration between a pair of images based on the affineregistration between them. In this way, effective atlas selection basedon affine registrations may be achieved, and deformable registration isonly necessary for the selected atlases. Alternatively, both atlases andtesting images may be registered and warped into a common templatespace. In this way, only one deformable registration is required at thetesting stage.

One drawback of various fast label propagation techniques is theinferior accuracy. Hence, such approaches obtain speed improvement bysacrificing segmentation accuracy. Various techniques such as thosedescribed above may be combined with fast label propagation, but suchcombinations do not mitigate the loss in accuracy.

In order to address the computation issue, various methods according tothe present disclosure combine multi-atlas segmentation withlearning-based segmentation. In general, learning-based segmentation isfaster, but less accurate than multi-atlas segmentation. However, whenapplied to correct errors produced by multi-atlas segmentation,learning-based segmentation produces accurate segmentation as well.According to various methods herein, the computational burden is reducedby performing multi-atlas segmentation with pairwise deformableregistration in a downsampled coarse scale space, followed bylearning-based error correction in the original space.

As set forth below, performance of multi-atlas segmentation may vary atdifferent scales and the native scale may not be optimal for producingthe most accurate segmentation. Applying multi-atlas segmentation in acoarse scale followed by learning-based error correction in the nativescale space can significantly reduce the overall computational cost,without sacrificing segmentation accuracy. In order to maximally utilizethe information provided from images, image analysis is usuallyconducted either in the native acquisition space or in a space close tothe acquisition space. However, working in a downsampled space may stillbe preferable in some circumstances, as described further herein.

Since the computational cost and memory requirement for both deformableimage registration and label fusion are proportional to the size of theprocessed image, one immediate advantage of applying multi-atlassegmentation in a downsampled coarse scale space is the speedimprovement and memory reduction. Furthermore, working in a coarse scalemay be beneficial for labeling some anatomical structures as well.Different anatomical structures may be best distinguishable at differentscales. For example, clinicians often need to switch the image viewer atdifferent scales for determining the boundaries of certain anatomystructures. In general, working in low resolution may be optimal forlabeling large scale anatomical structures, while high resolution may bemore suitable for labeling small scale structures.

Image downsampling is an information loss process. Hence, multi-atlassegmentation in a coarse resolution space potentially may causeadditional errors. For example, as shown in FIG. 2C, discussed furtherbelow, the anatomy boundaries produced in low resolution space are toocoarse to align accurately with the anatomy boundary in the originalimage space. However, since resolution change is a systematic change,such errors can be effectively corrected by learning-based errorcorrection, as described below.

In order to apply multi-atlas segmentation in a low resolution space,all training images and their segmentations are downsampled to have thesame coarse spatial resolution. Given a testing image, it is downsampledinto the target spatial resolution as well. After applying imageregistration and label fusion in the low resolution space, the producedresults are resampled back to the native space of the testing image.

Automatic segmentation algorithms may produce systematic errors incomparison to the gold standard manual segmentation. Such systematicerrors may be produced due to the limitations of the segmentation modelemployed by the method or due to suboptimal solutions produced by theoptimization algorithm. Systematic errors may also be produced due tothe fact that segmentation is produced in a low resolution space. Toreduce such systematic errors, corrective learning according toembodiments of the present disclosure applies machine learningtechniques to automatically detect and correct systematic errorsproduced by a host automatic segmentation method.

Various corrective learning algorithms are suitable for use according tothe present disclosure, including those available through the AdvancedNormalization Tools (ANTs) project. In some embodiments, a random forestclassifier is used. In other embodiments, an adaboost classifier isused. Corrective learning can be naturally combined with multi-atlassegmentation because no additional training images other than theatlases are required. For each training image, its low resolutionmulti-atlas segmentation is produced by using the remaining trainingimages as the atlases.

Subject images may be acquired using a variety of imaging devices knownin the art. For example, cardiac CT studies may be acquired by a SiemensCT Scanner such as the SOMATOM Definition Flash. In the examples herein,CT images are axially acquired. Each image has isotropic in-planeresolutions, varying from 0.55 mm² to 0.80 mm². The slice thicknessvaries from 0.8 mm to 2.0 mm. A histogram equalization is applied toeach image to standardize the intensity scale. The histogram equalizedimages are then resampled to have a 1 mm³ isotropic resolution.

Referring to FIG. 1, an exemplary CT image is shown. The base image ofFIG. 1A is manually traced by a clinician using existing commercialsoftware to yield labeled FIG. 1B. In this example, Amira is used tolabel for 28 cases: sternum, aorta (ascending/descending/arch/root),pulmonary artery (left/right/trunk), vertebrae, left/right atrium,left/right ventricle, left ventricular myocardium, superior/inferiorvena cava, and aortic/tricuspid/pulmonary/mitral valve.

In the following summary of an exemplary implementation, multi-atlassegmentation in two isotropic low spatial resolutions, 3 mm³ and 5 mm³,respectively are examined. Each image and its manual segmentation areresampled into the two spatial resolutions. For each of the threeresolutions, a four-fold crossvalidation on the 28 cases is conducted.For each cross-validation test, 7 images are selected for testing andthe remaining 21 images are applied as training images, i.e., atlases.The reported results are averaged from the four cross-validationexperiments.

The image-based registration is computed using the AdvancedNormalization Tools (ANTs) software. The registration sequentiallyoptimizes an affine transform and a deformable transform (Syn) betweenthe pair of images, using the Mattes mutual information metric. Thegradient step is set to 0.1. Three resolution levels with maximum 200iterations at the coarse level, 100 iterations at the middle level and50 iterations at the finest level are applied. With these parameters,registering a pair of images at the native 1 mm resolution (˜300×300×200image size) takes about 4 hours. Registering a pair of images at 3 mm(˜100×100×70) and 5 mm (˜60×60×40) resolutions takes about 203 and 54seconds, respectively.

Image similarity based local weighted voting is applied for combiningthe candidate segmentations produced by different atlases for the sametarget image. The voting weights are computed using the joint labelfusion technique. The joint label fusion software distributed from ANTsis applied with the default parameters. The produced low resolutionsegmentation is then resampled back to 1 mm resolution. Applying jointlabel fusion using 20 atlases to process one image takes about 210minutes at 1 mm resolution, 10 and 2.5 minutes at 3 mm and 5 mmresolution, respectively.

The dilation diameter is set to 5 mm for generating ROI for each label.Each random forest classifier is set to have 20 trees. The maximal depthof each tree is set to 20. With these parameters, the Javaimplementation takes about 20 minutes to process one image at the native1 mm resolution.

In summary, the total processing time for applying 20 atlases to segmenta single image at 1 mm, 3 mm, and 5 mm resolution is 5230 minutes, 98minutes, and 40 minutes, respectively. Applying multi-atlas segmentationat 5 mm resolution followed by error correction in the native space isabout 130 times faster than applying both multi-atlas segmentation andcorrective learning at 1 mm resolution. If the twenty pairwisedeformable registrations are computed fully in parallel, processing oneimage takes about 480 minutes, 33 minutes, and 23.5 minutes at the threeresolutions, respectively. Working at the 5 mm resolution is about 20times faster than working at 1 mm resolution.

Referring to FIGS. 2A-C, segmentations produced by multi-atlas labelfusion (MALF) in three spatial resolutions are shown for one subject. InFIG. 2A, the input image is downsampled to 1 mm resolution. In FIG. 2B,the input image is downsampled to 3 mm resolution. In FIG. 2C, the inputimage is downsampled to 5 mm resolution. It will be apparent thatperforming segmentation at lower resolutions results in unsuitablycoarse labelling.

For comparison, FIGS. 3A-C show the segmentations produced bymulti-atlas label fusion with learning-based error correction (MALF+CL)in three spatial resolutions for the same subject. In FIG. 3A, the inputimage is downsampled to 1 mm resolution before labeling. In FIG. 2B, theinput image is downsampled to 3 mm resolution before labeling. In FIG.2C, the input image is downsampled to 5 mm resolution before labeling.

Referring to FIG. 4, a 3D surface rendering of the manual segmentationsproduced for the same subject is shown. FIGS. 5A-C shows the 3D surfacerendering of the segmentations produced for the same subject at 1 mm, 3mm, and 5 mm, respectively. The segmentations produced at 3 mm and 5 mmresolution have inaccurate boundaries. The stepwise boundaries aretypical patterns obtained from upsampling from a low resolution to ahigh resolution.

Referring to FIGS. 6A-C, this effect is completely removed afterapplying learning-based error correction. This result demonstrates thatthe inaccurate boundaries produced due to applying MALF in a downsampledspace can be effectively corrected through learning-based errorcorrection.

Table 1 summarizes segmentation accuracy produced by applying MALF atdifferent spatial resolutions. The best performance is produced bydownsampling the images to 3 mm resolution. One explanation is thatimage registration is more likely to be stuck in local optimal solutionswhen it is computed in a high resolution space. In finer resolutionspaces, the searching range for correspondence matching is larger as thesame amount of deformation will result in larger voxel-displacements,making it more difficult to find the correct correspondence. Conversely,finding correct correspondences may become more difficult at too coarsespatial resolutions as well because visibility of anatomical featuresmay be compromised at low resolutions. Thus, an intermediate spatialresolution is optimal for MALF applications.

TABLE 1 MALF/MALF + CL 1 mm 3 mm 5 mm sternum 0.743/0.764 0.760/0.7820.683/0.749 ascending aorta 0.722/0.741 0.757/0.755 0.736/0.746descending aorta 0.782/0.822 0.829/0.846 0.809/0.850 aortic arch0.520/0.532 0.545/0.560 0.536/0.562 aortic root 0.513/0.531 0.589/0.5930.550/0.578 pulmonary artery trunk 0.757/0.767 0.803/0.808 0.768/0.803pulmonary artery right 0.830/0.842 0.838/0.862 0.787/0.845 pulmonaryartery left 0.771/0.791 0.791/0.812 0.726/0.797 vertebrae 0.869/0.8810.880/0.888 0.866/0.885 right atrium 0.794/0.823 0.850/0.865 0.831/0.861left atrium 0.861/0.885 0.884/0.897 0.861/0.892 right ventricle0.752/0.809 0.845/0.857 0.829/0.857 left ventricle 0.688/0.7320.736/0.750 0.714/0.748 myocardium 0.790/0.836 0.821/0.854 0.788/0.849aortic valve 0.371/0.375 0.368/0.426 0.310/0.370 pulmonary valve0.137/0.126 0.278/0.363 0.232/0.340 tricuspid valve 0.074/0.0730.066/0.058 0.017/0.010 mitral valve 0.372/0.440 0.512/0.559 0.338/0.458All 0.631/0.655 0.678/0.700 0.635/0.682

As shown in Table 1, segmentation accuracy (Dice similarity coefficient)was produced by MALF/MALF+CL when MALF is applied in different spatialresolutions. The results were computed by comparing automaticsegmentation with manual segmentation in the 1 mm resolution space.

With corrective learning applied, the most improvement, 4.7%, isobtained when MALF is applied in 5 mm resolution. This application ofMALF at a low spatial resolution produces systematic errors, which canbe effectively corrected by learning-based error correction. The overallsegmentation accuracy produced by applying MALF+CL at 3 mm and 5 mmresolution both outperformed applying MALF+CL at 1 mm resolution.

Referring to FIG. 7, an exemplary segmentation pipeline according toembodiments of the present disclosure is illustrated. Target image 701,as well as training atlas images 702 . . . 703 are downsampled 704.Pairwise deformable registration 705 is applied between target image 701and each of training atlas images 702 . . . 703. Based on theregistrations, candidate segmentations 706 are produced for the targetimage by warping the label from each of the atlases. Joint label fusion707 is applied to produce an initial segmentation 708 for the targetimage. The resulting segmentation is upsampled 709 to the originalresolution of the input image. A sequential learning algorithm isapplied 710 to correct segmentation errors produced by joint labelfusion to generate the final output 711.

Referring to FIG. 8, the present disclosure provides for methods toaddress the high computational burden of deformable registration basedmulti-atlas segmentation. A target image is downsampled 801 from itsoriginal high resolution image space to a low resolution space.Multi-atlas segmentation is applied 802 to produce an initialsegmentation for the target image in the downsampled low-resolutionspace. In some embodiments, label fusion 803 is performed. The resultingsegmentation is then refined by learning-based error correction 804 inthe native target image space.

In a cardiac CT segmentation application, where the native imaging spacehas about 1 mm³ spatial resolution, applying deformable registrationbased multi-atlas segmentation at about 5 mm³ spatial resolution isabout 130 times faster than applying MALF in the native (about 1 mm³)resolution space. In addition to speed gains, working with a downsampledspace may produce more accurate segmentation as well. This accuracy gainhas two main contributing factors: subsampled space may be optimal forcomputing globally optimal image registrations; and segmentation errorsproduced by applying MALF in a downsampled space can be effectivelycorrected by learning-based error correction.

When low resolution MALF is applied, learning-based error correctionbecomes the most time consuming step. To reduce the impact, errorcorrection may be implemented with parallelized computation to furtherreduce the overall processing time.

Referring now to FIG. 9, a schematic of an example of a computing nodeis shown. Computing node 10 is only one example of a suitable computingnode and is not intended to suggest any limitation as to the scope ofuse or functionality of embodiments of the invention described herein.Regardless, computing node 10 is capable of being implemented and/orperforming any of the functionality set forth hereinabove.

In computing node 10 there is a computer system/server 12, which isoperational with numerous other general purpose or special purposecomputing system environments or configurations. Examples of well-knowncomputing systems, environments, and/or configurations that may besuitable for use with computer system/server 12 include, but are notlimited to, personal computer systems, server computer systems, thinclients, thick clients, handheld or laptop devices, multiprocessorsystems, microprocessor-based systems, set top boxes, programmableconsumer electronics, network PCs, minicomputer systems, mainframecomputer systems, and distributed cloud computing environments thatinclude any of the above systems or devices, and the like.

Computer system/server 12 may be described in the general context ofcomputer system-executable instructions, such as program modules, beingexecuted by a computer system. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system/server 12 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 9, computer system/server 12 in computing node 10 isshown in the form of a general-purpose computing device. The componentsof computer system/server 12 may include, but are not limited to, one ormore processors or processing units 16, a system memory 28, and a bus 18that couples various system components including system memory 28 toprocessor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnect (PCI) bus.

Computer system/server 12 typically includes a variety of computersystem readable media. Such media may be any available media that isaccessible by computer system/server 12, and it includes both volatileand non-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system/server 12 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the invention.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system/server 12 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 12; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 12 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system/server 12 cancommunicate with one or more networks such as a local area network(LAN), a general wide area network (WAN), and/or a public network (e.g.,the Internet) via network adapter 20. As depicted, network adapter 20communicates with the other components of computer system/server 12 viabus 18. It should be understood that although not shown, other hardwareand/or software components could be used in conjunction with computersystem/server 12. Examples, include, but are not limited to: microcode,device drivers, redundant processing units, external disk drive arrays,RAID systems, tape drives, and data archival storage systems, etc.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

The descriptions of the various embodiments of the present inventionhave been presented for purposes of illustration, but are not intendedto be exhaustive or limited to the embodiments disclosed. Manymodifications and variations will be apparent to those of ordinary skillin the art without departing from the scope and spirit of the describedembodiments. The terminology used herein was chosen to best explain theprinciples of the embodiments, the practical application or technicalimprovement over technologies found in the marketplace, or to enableothers of ordinary skill in the art to understand the embodimentsdisclosed herein.

What is claimed is:
 1. A method, comprising: resampling a target imageand each one of a series of training images from an original resolutionto a lower resolution; after resampling, comparing the target imageagainst each one of the series of training images, through registrationand warping, thereby producing a labeled target image for each of thetraining images; averaging the labeled target images to form a consensuslabeled target image; resampling the consensus labeled target image fromits original resolution to a higher resolution; and applying errorcorrection to the consensus labeled target image, thereby forming afinalized labeled target image.
 2. The method of claim 1, wherein theseries of training images comprises labeled atlases.
 3. The method ofclaim 1, wherein averaging the labeled target images comprises applyingjoint label fusion.
 4. The method of claim 1, wherein applying errorcorrection comprises applying a corrective learning algorithm.
 5. Themethod of claim 4, wherein the corrective learning algorithm comprises arandom forest classifier.
 6. The method of claim 4, wherein thecorrective learning algorithm comprises an adaboost classifier.
 7. Asystem for anatomy segmentation, comprising one or more computing node,the one or more computing node being adapted to perform a methodcomprising: resampling a target image and each one of a series oftraining images from an original resolution to a lower resolution; afterresampling, comparing the target image against each one of the series oftraining images, through registration and warping, thereby producing alabeled target image for each of the training images; averaging thelabeled target images to form a consensus labeled target image;resampling the consensus labeled target image from its originalresolution to a higher resolution; and applying error correction to theconsensus labeled target image, thereby forming a finalized labeledtarget image.
 8. The system of claim 7, wherein the series of trainingimages comprises labeled atlases.
 9. The system of claim 7, whereinaveraging the labeled target images comprises applying joint labelfusion.
 10. The system of claim 7, wherein applying error correctioncomprises applying a corrective learning algorithm.
 11. The system ofclaim 10, wherein the corrective learning algorithm comprises a randomforest classifier.
 12. The system of claim 10, wherein the correctivelearning algorithm comprises an adaboost classifier.
 13. A computerprogram product for anatomy segmentation, the computer program productcomprising a computer readable storage medium having programinstructions embodied therewith, the program instructions executable bya processor to cause the processor to perform a method comprising:resampling a target image and each one of a series of training imagesfrom an original resolution to a lower resolution; after resampling,comparing the target image against each one of the series of trainingimages, through registration and warping, thereby producing a labeledtarget image for each of the training images; averaging the labeledtarget images to form a consensus labeled target image; resampling theconsensus labeled target image from its original resolution to a higherresolution; and applying error correction to the consensus labeledtarget image, thereby forming a finalized labeled target image.
 14. Thecomputer program product of claim 13, wherein the series of trainingimages comprises labeled atlases.
 15. The computer program product ofclaim 13, wherein averaging the labeled target images comprises applyingjoint label fusion.
 16. The computer program product of claim 13,wherein applying error correction comprises applying a correctivelearning algorithm.
 17. The computer program product of claim 16,wherein the corrective learning algorithm comprises a random forestclassifier.
 18. The computer program product of claim 16, wherein thecorrective learning algorithm comprises an adaboost classifier.